Literature DB >> 26004695

Preliminary investigation into sources of uncertainty in quantitative imaging features.

Xenia Fave1, Molly Cook2, Amy Frederick3, Lifei Zhang4, Jinzhong Yang4, David Fried5, Francesco Stingo6, Laurence Court4.   

Abstract

Several recent studies have demonstrated the potential for quantitative imaging features to classify non-small cell lung cancer (NSCLC) patients as high or low risk. However applying the results from one institution to another has been difficult because of the variations in imaging techniques and feature measurement. Our study was designed to determine the effect of some of these sources of uncertainty on image features extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC) tumors. CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current). Texture values did not vary substantially with the choice of breathing phase; however, almost half (12 out of 28) of the measured textures did change significantly when measured from the average images compared to the end-of-exhale phase. Of the 28 features, 8 showed a significant variation when measured from the largest cross sectional slice compared to the entire tumor, but 14 were correlated to the entire tumor value. While simulating a decrease in tube voltage had a negligible impact on texture features, simulating a decrease in mA resulted in significant changes for 13 of the 23 texture values. Our results suggest that substantial variation exists when textures are measured under different conditions, and thus the development of a texture analysis standard would be beneficial for comparing features between patients and institutions.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26004695     DOI: 10.1016/j.compmedimag.2015.04.006

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  39 in total

1.  Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography.

Authors:  Dennis Mackin; Rachel Ger; Skylar Gay; Cristina Dodge; Lifei Zhang; Jinzhong Yang; Aaron Kyle Jones; Laurence Court
Journal:  Invest Radiol       Date:  2019-05       Impact factor: 6.016

2.  Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study.

Authors:  Usman Mahmood; Aditya P Apte; Joseph O Deasy; C Ross Schmidtlein; Amita Shukla-Dave
Journal:  J Comput Assist Tomogr       Date:  2017 Nov/Dec       Impact factor: 1.826

3.  AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

Authors:  Isabella Castiglioni; Francesca Gallivanone; Paolo Soda; Michele Avanzo; Joseph Stancanello; Marco Aiello; Matteo Interlenghi; Marco Salvatore
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-11       Impact factor: 9.236

4.  Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom.

Authors:  K Buch; B Li; M M Qureshi; H Kuno; S W Anderson; O Sakai
Journal:  AJNR Am J Neuroradiol       Date:  2017-03-24       Impact factor: 3.825

Review 5.  NCTN Assessment on Current Applications of Radiomics in Oncology.

Authors:  Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-01-31       Impact factor: 7.038

6.  Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis.

Authors:  Rachel B Ger; Daniel F Craft; Dennis S Mackin; Shouhao Zhou; Rick R Layman; A Kyle Jones; Hesham Elhalawani; Clifton D Fuller; Rebecca M Howell; Heng Li; R Jason Stafford; Laurence E Court
Journal:  Comput Med Imaging Graph       Date:  2018-09-15       Impact factor: 4.790

7.  Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features.

Authors:  Ruichuan Shi; Weixing Chen; Bowen Yang; Jinglei Qu; Yu Cheng; Zhitu Zhu; Yu Gao; Qian Wang; Yunpeng Liu; Zhi Li; Xiujuan Qu
Journal:  Am J Cancer Res       Date:  2020-12-01       Impact factor: 6.166

8.  Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics.

Authors:  Rachel B Ger; Carlos E Cardenas; Brian M Anderson; Jinzhong Yang; Dennis S Mackin; Lifei Zhang; Laurence E Court
Journal:  J Vis Exp       Date:  2018-01-08       Impact factor: 1.355

9.  Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.

Authors:  Jinzhong Yang; Lifei Zhang; Xenia J Fave; David V Fried; Francesco C Stingo; Chaan S Ng; Laurence E Court
Journal:  Comput Med Imaging Graph       Date:  2015-12-14       Impact factor: 4.790

Review 10.  Applications and limitations of radiomics.

Authors:  Stephen S F Yip; Hugo J W L Aerts
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

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